AMI  Vol.5 No.3 , July 2015
Multiparameteric PET-MR Assessment of Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: PET, MR, PET-MR and Tumor Texture Analysis: A Pilot Study
ABSTRACT
Purpose: Patients with locally advanced rectal cancer (LARC) achieving pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (CRT) have significantly improved long term survival. Preoperative detection of pCR may enable a conservative therapeutic approach in some patients. The purpose of the current prospective pilot study was to assess multiparametric qualitative and quantitative MR, PET, PET-MR and tumor texture features in predicting pCR to CRT in patients with LARC. Material and Methods: Eighteen LARC patients underwent staging with FDG-PET and MR-rectum and 15 had post-CRT restaging. Response was assessed qualitatively and quantitatively. SUV (tumor/background), SUV/ADC, and tumor texture parameters derived via machine learning algorithms (MLA) from PET and multiple MR sequences and were correlated with histopathology. Results: A third of patients had pCR. Sensitivity, specificity & accuracy of PET, MR and combined PET-MR were 90, 60, & 80; 90, 20 & 66.7; 90, 80 & 86.7, respectively. Differences did not reach statistical significance. Quantitatively, only tumor-muscle (SUV/ADC) ratio improved prediction of pCR. Of all texture features assessed using MLA, only the classifier trained on pre-treatment PET was significant (p = 0.034; accuracy, 92.8%). Combined PET and MR texture features did not improve performance. Conclusion: Combined PET-MR may improve specificity compared with PET or MR alone, although this needs to be validated in a larger cohort. Tumor to muscle SUV/ADC ratios post-therapy and texture features on baseline PET show promise in improving prediction of pCR post-CRT in LARC.

Cite this paper
Metser, U. , Jhaveri, K. , Murphy, G. , Halankar, J. , Hussey, D. , Dufort, P. and Kennedy, E. (2015) Multiparameteric PET-MR Assessment of Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: PET, MR, PET-MR and Tumor Texture Analysis: A Pilot Study. Advances in Molecular Imaging, 5, 49-60. doi: 10.4236/ami.2015.53005.
References
[1]   Maas, M., Nelemans, P.J., Valentini, V., Das, P., Rödel, C., Kuo, L.J., et al. (2010) Long-Term Outcome in Patients with a Pathological Complete Response after Chemoradiation for Rectal Cancer: A Pooled Analysis of Individual Patient Data. Lancet Oncology, 11, 835-844.
http://dx.doi.org/10.1016/S1470-2045(10)70172-8

[2]   Zorcolo, L., Rosman, A.S., Restivo, A., Pisano, M., Nigri, G.R., Fancellu, A., et al. (2012) Complete Pathologic Response after Combined Modality Treatment for Rectal Cancer and Long-Term Survival: A Meta-Analysis. Annals of Surgical Oncology, 19, 2822-2832.
http://dx.doi.org/10.1245/s10434-011-2209-y

[3]   Martin, S.T., Heneghan, H.M. and Winter, D.C. (2012) Systematic Review and Meta-Analysis of Outcomes Following Pathological Complete Response to Neoadjuvant Chemoradiotherapy for Rectal Cancer. British Journal of Surgery, 99, 918-928. http://dx.doi.org/10.1002/bjs.8702

[4]   Habr-Gama, A., Perez, R., Proscurshim, I. and Gama-Rodrigues, J. (2010) Complete Clinical Response after Neoadjuvant Chemoradiation for Distal Rectal Cancer. Surgical Oncology Clinics of North America, 19, 829-845. http://dx.doi.org/10.1016/j.soc.2010.08.001

[5]   Maas, M., Beets-Tan, R.G.H., Lambregts, D.M., Lammering, G., Nelemans, P.J., Engelen, S.M., et al. (2011) Wait-and-See Policy for Clinical Complete Responders after Chemoradiation for Rectal Cancer. Journal of Clinical Oncology, 29, 4633-4640. http://dx.doi.org/10.1200/JCO.2011.37.7176

[6]   Patel, U.B., Blomqvist, L.K., Taylor, F., George, C., Gurthrie, A., Bees, N., et al. (2012) MRI after Treatment of Locally Advanced Rectal Cancer: How to Report Tumor Response—The MERCURY Experience. American Journal of Roentgenology, 199, W486-W495.
http://dx.doi.org/10.2214/ajr.11.8210

[7]   Patel, U.B., Taylor, F., Blomqvist, L., George, C., Evans, H., Tekkis, P., et al. (2011) Magnetic Resonance Imaging-Detected Tumor Response for Locally Advanced Rectal Cancer Predicts Survival Outcomes: MERCURY Experience. Journal of Clinical Oncology, 29, 3753-3760.
http://dx.doi.org/10.1200/JCO.2011.34.9068

[8]   Mandard, A., Dalibard, F., Mandard, J., Marnay, J., Henry-Amar, M., Petiot, J.F., et al. (1994) Pathologic Assessment of Tumor Regression after Preoperative Chemoradiotherapy of Esophageal Carcinoma. Clinicopathologic Correlations. Cancer, 73, 2680-2686.
http://dx.doi.org/10.1002/1097-0142(19940601)73:11<2680::AID-CNCR2820731105>3.0.CO;2-C

[9]   Ryan, R., Gibbons, D., Hyland, J.M., Treanor, D., White, A., Mulcahy, H.E., et al. (2005) Pathological Response Following Long-Course Neoadjuvant Chemoradiotherapy for Locally Advanced Rectal Cancer. Histopathology, 47, 141-146. http://dx.doi.org/10.1111/j.1365-2559.2005.02176.x

[10]   Tixier, F., Le Rest, C.C., Hatt, M., Albarghach, N., Pradier, O., Metges, J.P., et al. (2011) Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer. Journal of Nuclear Medicine, 52, 369-378.
http://dx.doi.org/10.2967/jnumed.110.082404

[11]   Haralick, M.H., Shanmugam, K. and Dinstein, I. (1973) Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, 610-621.
http://dx.doi.org/10.1109/TSMC.1973.4309314

[12]   Amadasun, M. and King, R. (1989) Texural Features Corresponding to Textural Properties. IEEE Transactions on Systems, Man, and Cybernetics, 19, 1264-1274. http://dx.doi.org/10.1109/21.44046

[13]   Loh, H.H., Leu, G. and Luo, R.C. (1988) The Analysis of Natural Textures Using Run Length Features. IEEE Transactions on Industrial Electronics, 35, 323-328. http://dx.doi.org/10.1109/41.192665

[14]   Thibault, G., Fertil, B., Navarro, C. and Pereira, S. (2009) Texture Indexes and Gray Level Size Zone Matrix Application to Cell Nuclei Classification. Pattern Recognition and Information Processing (PRIP), 140-145.

[15]   Chang, C.C. and Lin, C.J. (2011) LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology, 2, 1-27. http://dx.doi.org/10.1145/1961189.1961199

[16]   Li, Y.L., Wu, L.M., Chen, X.X., Delproposto, Z., Hu, J.N. and Xu, J.R. (2014) Is Diffusion-Weighted MRI Superior to FDG-PET or FDG-PET/CT in Evaluating and Predicting Pathological Response to Preoperative Neoadjuvant Therapy in Patients with Rectal Cancer? Journal of Digestive Diseases, 15, 525-537.
http://dx.doi.org/10.1111/1751-2980.12174

[17]   Rakheja, R., De Mello, L., Chandarana, H., Jackson, K., Geppert, C., Faul, D., et al. (2013) Comparison of the Accuracy of PET/CT and PET/MRI Spatial Registration of Multiple Metastatic Lesions. American Journal of Roentgenology, 201, 1120-1123. http://dx.doi.org/10.2214/AJR.13.11305

[18]   Park, H., Wood, D., Hussain, H., Meyer, C.R., Shah, R.B., Johnson, T.D., et al. (2012) Introducing Parametric Fusion PET/MRI of Primary Prostate Cancer. Journal of Nuclear Medicine, 53, 546-551.
http://dx.doi.org/10.2967/jnumed.111.091421

[19]   Gevaert, O., Xu, J., Hoang, C.D., Leung, A.N., Xu, Y., Quon, A., et al. (2012) Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results. Radiology, 264, 397-396.
http://dx.doi.org/10.1148/radiol.12111607

[20]   Karlo, C., Paolo, P.L., Chaim, J., Hakimi, A.A., Ostrovnaya, I., Russo, P., et al. (2013) Radiogenomics of Clear Cell Carcinoma: Association between CT Imaging Features and Mutations. Radiology, 270, 464-471. http://dx.doi.org/10.1148/radiol.13130663

[21]   Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S.A., Schabath, M.B., et al. (2012) Radiomics: The Process and the Challenges. Magnetic Resonance Imaging, 30, 1234-1248.
http://dx.doi.org/10.1016/j.mri.2012.06.010

 
 
Top